Horn River Basin Research Articles

Semi-Analytical Reservoir Modeling of Non-Linear Gas Diffusion with Gas Desorption Applied to the Horn River Basin Shale Gas Play, British Columbia (Canada)

Adsorbed gas may account for a significant part of the gas resources in shale gas and coalbed methane plays. Understanding gas sorption behaviors and integrating gas desorption into analytical reservoir modeling and an associated transient performance analysis are important for evaluating a system’s gas desorption ability and further analyzing its CO2 injectability, utilization, and storage capacity. However, gas desorption, along with other pressure-dominated gas properties, increases a system’s non-linearity in theoretical studies. Few studies on analytical modeling have integrated the gas desorption feature into a non-linear system and validated the model’s accuracy. In this study, the desorbed gas due to pressure decay was treated as an additional source/sink term in the source-and-sink function methods. This method was combined with the integral image method in a semi-analytical manner to determine the amount of gas desorption. Fundamental reservoir and gas properties from the Horn River Basin shale gas play were chosen to evaluate the methodology and the performance of the associated production well. The results were compared with the commercial fine-gridding numerical simulation software, and good matches were achieved. The results showed that the desorbed gas released from rock will supply free-gas flow when the pressure significantly decreases due to gas production. The production wellbore pressure can be maintained at a higher level, and the production rate was higher than in cases where gas desorption was not considered, depending on the operating conditions.

Mineralogical Characterization From Geophysical Well Logs Using a Machine Learning Approach: Case Study for the Horn River Basin, Canada

AbstractAccurate estimation of mineral composition is essential for refined reservoir characterization, thermal conductivity and mechanical determinations of sedimentary rocks, but is extremely challenging in shale units due to the mineralogical complexity, low porosity and ultra‐low permeability. Direct mineral measurements derived from laboratory X‐ray diffraction (XRD) analysis on core samples and borehole geochemical logging tool (GLT), and conventional geophysical logs from vertical wells penetrating sediments are widely available in some basins, which enables detailed mineralogical characterization of a well. A hybrid machine learning (ML) architecture that improves model training and validation by combining convolutional neural network (CNN) with XGBoost allows accurate description of the mineralogical compositions across a basin. We applied this ML approach to predict the mineral compositions using conventional well logs from the Horn River Basin, northeast British Columbia, Canada, where extensive drilling for shale‐gas and conventional hydrocarbon resources, complemented by high temperature geothermal energy potential is ideal for case testing. The predicted mineral compositions from the ML approach are consistent with the mineralogical readings from the GLT and are confirmed by the XRD mineral measurements. This allows basin‐wide mineral compositions mapping that reveals spatial trends of major mineral compositions and their relationship with the previously recognized geomechanical and geological features, which have important implications for thermal conductivity modeling, reservoir evaluation and extensive geological studies.

Hybrid geothermal energy and hydrocarbon resources production by repurposing horizontal wells in shale gas reservoirs in Horn River Basin, British Columbia, Canada

The production of geothermal energy from the depleted oil and gas reservoirs could represent one of the practical pathways of the clean energy transition for the hydrocarbon energy industry. In this study, a new strategy for repurposing oil and gas wells into geothermal energy production system is proposed to co-produce hydrocarbon and geothermal resources during the transition. At a certain stage in a hydrocarbon production system of fractured horizontal wellbores, one vertical injection well is added to connect to the end of the horizontal wellbores to form a U-loop system with open fractures in the lateral part. Cold water is injected through the injection well, transported along the U-loop system, and geothermal energy is produced along with natural gas for utilization. Natural gas production continues as long as the pressure of the horizontal well remains lower than the reservoir pressure. An example application case from a typical shale gas production system in the Horn River Basin of British Columbia, Canada has been examined in this study. The results show that reduction in cumulative gas production is limited if the injection well is added at a middle or later stage. The outlet temperature from this system behaves like those in a closed-loop geothermal system and could be stable for several decades of operation. Although the time of conversion is sensitive to gas production, it does not affect the bottom hole temperature much. The water flow rate does not affect the cumulative gas production but considerably affects the formation temperature. The horizontal length of the fractured wellbore will significantly affect the gas production performance and geothermal energy output. Fracture length and permeability have critical impact on gas production but less influence on geothermal energy recovery.

Nanoindentation of Horn River Basin Shales: The Micromechanical Contrast Between Overburden and Reservoir Formations

AbstractWe present a micromechanical characterization of shales from the Horn River Basin, NW Canada. The shales have contrasting mineralogy and microstructures and play different geomechanical roles in the field: the sample set covers an unconventional gas reservoir and the overburden unit that serves as the upper fracture barrier. Composition and texture were characterized using X‐ray diffraction, mercury injection porosimetry, and scanning electron microscopy (SEM). Grid nanoindentation testing was used to obtain the mechanical response of the dominant phases in the shale microstructure. Samples were indented parallel and perpendicular to the bedding plane to assess mechanical anisotropy. Chemical analysis of the grids with SEM‐EDS (energy dispersive X‐ray spectroscopy) was undertaken and the coupled chemo‐mechanical data was used in a statistical clustering procedure (Gaussian mixture model) to reveal the mechanical properties of each phase. The results show that the overburden consists of a soft clay matrix with highly anisotropic elastic stiffness, and stiffer but effectively isotropic inclusions of quartz and feldspar; the significant anisotropy of the overburden has been previously observed on a much larger scale using microseismic data. Creep displacement is concentrated in the clay matrix, which is the key phase for fracture barrier and seal applications. The reservoir units are harder and have more isotropic mechanical responses, primarily due to their lower clay content. Despite varied compositions and microstructures, the major phases of these shales (clay/organic matrix, quartz/feldspar, dolomite, and calcite) have unique mechanical signatures, which will aid identification in future micromechanical characterizations and facilitate their use in upscaling schemes.

Prediction of Total Organic Carbon in Organic-Rich Shale Rocks Using Thermal Neutron Parameters.

Total organic carbon (TOC) content is one of the crucial parameters that determine the value of the source rock. The TOC content gives important indications about the source rocks and hydrocarbon volume. Various techniques have been utilized for TOC quantification, either by geochemical analysis of source rocks in laboratories or using well logs to develop mathematical correlations and advanced machine learning models. Laboratory methods require intense sampling intervals to have an accurate understanding of the reservoir, and depending on the thickness of the interested formation, it can be time-consuming and costly. Empirical correlations based on well logs (e.g., density, sonic, gamma ray, and resistivity) showed fast predictions and very reasonable accuracies. However, other important parameters such as thermal neutron logs have not been studied yet as a potential input for providing reliable TOC predictions. Also, different studies estimate the TOC based on the well-logging data for various formations; however, limited studies were reported to predict the TOC for the Horn River Formation. Therefore, the objective of this study is to estimate the TOC variations based on the thermal neutron logs using one of the largest source rocks in Canada: The Horn River Formation. More than 150 data sets were collected and used in this work. The parameters of the artificial neural network (ANN) model were fine-tuned in order to improve the model's prediction performance. Furthermore, an empirical correlation was developed utilizing the optimized ANN model to allow fast and direct application for the developed model. The developed correlation can predict the TOC with an average absolute error of 0.52 wt %. The proposed TOC model was able to outperform the previous models, and the coefficient of determination was increased from 0.28 to 0.73. Overall, the proposed TOC model can provide high accuracy for TOC ranges from 0.3 to 6.44 wt %. The developed model can provide a real-time quantification for the organic matter maturity, helping to allocate the zones of mature organic matter within the drilled formations.

Micromechanical characterisation of overburden shales in the Horn River Basin through nanoindentation

The paper presents a micromechanical characterisation of Fort Simpson shale, which overlies unconventional gas-producing lithologies in the Horn River Basin, NW Canada. The Fort Simpson formation is clay-rich and microseismic data recorded during hydraulic fracturing events in the underlying reservoir has shown the formation acts as a barrier to fracture development, with a notably anisotropic seismic response. Samples were prepared from core fragments and the composition and texture of the shale was characterised using X-ray diffraction, mercury injection porosimetry and scanning electron microscopy (SEM). Nanoindentation testing was used to obtain the mechanical response of the shale microstructure, at grain-scale. The indentation was conducted on a grid pattern and samples were oriented both parallel and perpendicular to the bedding plane to assess the inherent mechanical anisotropy. Chemical analysis of the grids was also undertaken through SEM/EDS (energy dispersive X-ray spectroscopy) and the coupled chemo-mechanical data was used to characterise the material phases of the shale through a statistical clustering procedure. The results show that Fort Simpson shale broadly consists of a soft clay phase, with strongly anisotropic elastic stiffness, and stiffer but effectively isotropic grains of quartz and feldspar. A simple upscaling scheme was also applied to link the grain-scale elastic stiffness to the field-scale microseismic data.

Prediction of liquid surge volumes and flow rates for gas wells using machine learning

Liquid surge refers to an excessive liquid inflow to a slug catcher or a separator and is one of the main issues in flow assurance. The wellhead choke valves of gas wells must be adjusted to maintain the target flow rate as the reservoir pressure drops. The wellhead choke opening can be determined by conducting multiphase pipeline transient flow simulations to achieve the target flow rate and avoid liquid surges. However, it is not financially and computationally practical to conduct many multiphase transient pipeline flow simulations simultaneously for hundreds of wells. We found that flow rates and maximum liquid surge volumes for gas wells can be predicted accurately using simple machine learning models when the wellhead choke valve is adjusted. The machine learning models provided accurate predictions (R2 > 0.99) regarding flow rates and maximum liquid surge volumes for different wellhead pressure drop plans, vertical and horizontal well lengths, and current operating conditions in cases involving two real fields, the Horn River Basin (HRB) and Rakhine Basin field. The wellhead operating conditions that are appropriate for achieving the target flow rate and avoiding liquid surges can be efficiently determined using the machine learning models instead of multiphase transient pipeline flow simulators. The proposed approach also accurately estimated downhole pressures that are necessary to evaluate reservoir performances given wellhead pressures and flow rates. Lastly, we presented what features are dominant in predicting liquid surge volumes, flow rates, and downhole pressures in feature importance analyses.

Verification and Application of Sequence Stratigraphy to Reservoir Characterization of Horn River Basin, Canada

Shale reservoirs, the most important unconventional resource, are difficult to characterize. Shale formations require detailed interpretation of geological, petrophysical, and geochemical analyses, and an integration of these disciplines. In terms of geological interpretation, the commonly used sequence stratigraphy analysis includes a lithofacies analysis. The application of sequence stratigraphy to shales facilitates the ability to relate between lithofacies and mineral composition, petrophysical parameters, and kerogen contents, which are affected by depositional setting. The classification of lithofacies is indispensable for reservoir quality prediction. In this study, porosity, permeability, and TOC content largely depend on lithofacies, and their correlation coefficient is relatively high. The sequence stratigraphic interpretation shows that organic carbon content usually increases within the maximum flooding surfaces and decreases stepwise. However, the relationship between total organic carbon contents and systems tract is less direct and redox dependent.

A Strategy for Choosing Red‐Light Thresholds to Manage Hydraulic Fracturing Induced Seismicity in North America

AbstractInduced earthquakes caused by hydraulic fracturing are a growing concern, with risks that are often managed by traffic light protocols (TLPs). Here, we apply a risk‐informed strategy for choosing TLP red‐light thresholds. We utilize a combination of probabilistic maximum magnitudes, formation depth, site amplification, ground motion relationships, felt/damaging shaking tolerances, and population information to simulate the spatial distribution of nuisance and damage impacts. We apply this approach to many of the prominent North American shale plays that have caused earthquakes: the Horn River Basin, the Duvernay Formation, the Montney Formation, the Utica Shale, the Marcellus Shale, the SCOOP & STACK play in Oklahoma, the Eagle Ford Formation, and the Delaware Basin. We find that induced earthquake impacts are spatially heterogeneous, depending most strongly on population density. These spatial heterogeneities vary on different length scales, depending on if they are related to nuisance (longer range, 100s kms) or damage (shorter range, 10s kms). Because of this variability, we suggest an approach that sets red‐light magnitudes based on tolerances to nuisance and damage risks (varying between Mw 2.0–5.0). Comparison of the results between North American shale plays where TLPs have been enacted suggests that nuisance risks have been an influencing factor in determining red‐light thresholds. Our method provides quantitative guidelines for traffic light protocols designed in a risk‐informed manner that retains the implementational simplicity of magnitude‐based thresholds. In addition, our results provide a benchmark for jurisdictional nuisance tolerances that future TLPs could be guided by.

Stress drops of hydraulic fracturing induced microseismicity in the Horn River basin: challenges at high frequencies recorded by borehole geophones

SUMMARYThe ground motions caused by seismicity associated with fluid injection can pose a significant hazard. Borehole geophone arrays can provide access to tiny seismic events, which can extend the investigated magnitude range. However, the high frequency phase arrivals (i.e., >100 Hz) also present challenges associated with high frequency cut-offs (fmax), stronger attenuation and resonances within geophones. These effects limit our ability to accurately constrain attenuation models and high frequency source parameters. We investigate 112 −0.6 $\le \, M_{\rm w}\, \le$ 0.7 seismic events and calculate corner frequencies and stress drops from 90 of these events recorded during hydraulic fracturing treatment in the Horn River basin, British Columbia. High frequency resonances (>250 Hz) caused by spurious frequency excitation and/or coupling issues can significantly distort the shape of phase arrival spectra and affect source parameter estimates. Critically, resonances vary in strength between (nearly) colocated events, which may compromise the validity of a spectral ratio approach. For stations showing the cleanest spectra, the Brune model provides a decent fit to the displacement spectra. However, bandwidth limitations, low signal-to-noise ratios, high frequency cut-offs and significant attenuation still hinder our ability to retrieve high frequency source parameters. We find that a frequency independent Qp = 180 ± 40 provides a reasonable model for crustal attenuation but the large uncertainty caused by resonances prevents a robust constraint. From those events that show the best fits, we find a mean Madariaga corner frequency of 210 Hz ± 30 from P-phase arrivals, which is in the range of expected values if self-similarity extends into negative magnitudes. We also calculate a mean stress drop of 1.6 MPa ± 1.2, which is within the tectonic range but slightly lower than other deeper regional studies, which can be explained by lower effective stresses and/or a lower crustal shear strength. We find no evidence for a change in stress drop with depth or distance from the point of injection. A plausible explanation is that effective stresses are lowered relatively quickly over the entire fault zone via direct hydraulic connections. However, the large uncertainties make it difficult to interpret source parameter variability in detail. For high resolution monitoring and source properties of microseismicity, there is an urgent need for high quality high frequency recordings unaffected by spurious frequencies.

Geochemistry of the Cretaceous Chinkeh oil from Maxhamish field and Garbutt black shale in the Liard Basin, Canada: Implications for a liquid-rich shale hydrocarbon resource

The Cretaceous Garbutt Formation shale and crude oils from the Cretaceous Chinkeh Formation in Liard Basin were subjected to geochemical analysis including biomarker fingerprinting. The Chinkeh oils are sweet light oils with API gravity and geochemical markers typical for the late stage oil window maturity. The oils show good correlation to the Garbutt shale extracts for their sterane compositions, while there are some minor discrepancies amongst their terpane distributions. These differences include enhanced contribution of tricyclic terpanes, high C23 tricyclic terpanes versus C30 hopane and low C24 tetracyclic terpane versus C26 tricyclic terpanes for the Chinkeh oil compared with the Garbutt shale extracts. Although not analyzed during this study, the Triassic Toad/Grayling interval, as stratigraphic equivalents of the Montney and Doig formations in Western Canada Sedimentary Basin may account for the terpane compositional features. Therefore, it is likely that the Chinkeh oils are mainly sourced from the Cretaceous Garbutt shale with minor additional contributions from a Triassic source. The Garbutt Shale is immature in the Horn River Basin, while in the Liard Basin in a north to south transect the unit's thermal maturity ranges from early mature oil window to wet gas/condensate window, and thus may be prospective for unconventional liquid hydrocarbon resource recovery.

Forecasting of Induced Seismicity Rates from Hydraulic Fracturing Activities Using Physics-Based Models for Probabilistic Seismic Hazard Analysis: A Case Study

One of the major challenges in seismic hazard analysis for induced seismicity is the forecasting of future seismicity rates, which are described by the Gutenberg–Richter parameters (a and b-values from the earthquake magnitude frequency distributions). In this study, we implement two methodologies in order to determine the Gutenberg–Richter parameters related to future induced seismicity: the Seismogenic Index and the Hydromechanical Nucleation model. We apply both methods in one recent case of induced seismicity: the Horn River Basin, Northeast B.C., Canada. We perform two tests to compare the predictions of both models with the observed seismicity. First, we compare the predicted number of earthquakes exceeding a certain magnitude per month with the observed number of earthquakes. In this test, both methods predict earthquake rates similar to the observed induced seismicity in the Horn River Basin. Second, we evaluate how appropriate are the predictions for specific magnitude ranges (given by forecasted Gutenberg–Richter parameters). In this case, both models make inaccurate predictions for specific magnitude ranges (annual magnitude frequency distributions), resulting in an under- or overestimation of the hazard but often with contradicting forecasts, despite using shared observations. The predictions under- and overestimate the hazard at different time points, due to the complexity in the evolution of the seismicity, and the assumption of constant b-values. As a result, these incorrect forecasts for future magnitude-frequency distributions lead to biased seismic hazard and ground motion predictions. More research effort is required in order to forecast more accurate Gutenberg–Richter parameters, particularly changes in the b-value, as observed in the Horn River Basin induced seismicity case.

Inference of Hydraulically Fractured Reservoir Properties from Production Data Using the Indicator-Based Probability Perturbation Assisted History-Matching Method

Hydraulically fractured horizontal wells are widely adopted for the development of tight or shale gas reservoirs. The presence of highly heterogeneous, multi-scale, fracture systems often renders any detailed characterization of the fracture properties challenging. Discrete fracture network (DFN) model offers a viable alternative for explicit representation of multiple fractures in the domain, where the comprising fracture properties are defined in accordance to specific probability distributions. However, even with the successful modelling of a DFN, the relationship between a set of fracture parameters and the corresponding production performance is highly nonlinear, implying that a robust history-matching workflow capable of updating the pertinent DFN model parameters is required for calibrating stochastic reservoir models to both geologic and dynamic production data.This paper proposes an integrated approach for the history matching of hydraulically fractured reservoirs. First, multiple realizations of the DFN model are constructed conditioning to available geological information such as seismic data and well logs, which are useful for inferring the prior probability distributions of relevant fracture parameters. Next, the models are upscaled into equivalent continuum dual-permeability models and subjected to numerical multiphase flow simulation. The predicted production performance is compared with the actual recorded responses. Finally, the DFN-model parameters are adjusted following an indicator-based probability perturbation method. Although the probability perturbation technique has been applied to update facies distributions in the past, its application in modeling DFN distributions is limited. To account for the non-Gaussian nature of the DFN parameters, an indicator formulation is proposed. The algorithm aims at minimizing the objective function, while reducing the uncertainties in the unknown fracture parameters.The method is applied to estimate the posterior probability distributions of the transmissivity of the primary fracture (Tpf), transmissivity of the secondary induced fracture (Tsf) and global fracture intensity (Psf32G) in a multifractured shale gas well in the Horn River Basin. An initial realization of the DFN model is sampled from the prior probability distributions using the Monte Carlo simulation. These probability distributions are updated to match the production history, and multiple equi-probable realizations of the DFN models are sampled from the updated (posterior) distributions accordingly. The final sampled realizations of the DFN model are consistent with both static geological information and dynamic production history.

Three-dimensional distinct element modeling of fault reactivation and induced seismicity due to hydraulic fracturing injection and backflow

Abstract This paper presents a three-dimensional fully hydro-mechanical coupled distinct element study on fault reactivation and induced seismicity due to hydraulic fracturing injection and subsequent backflow process, based on the geological data in Horn River Basin, Northeast British Columbia, Canada. The modeling results indicate that the maximum magnitude of seismic events appears at the fracturing stage. The increment of fluid volume in the fault determines the cumulative moment and maximum fault slippage, both of which are essentially proportional to the fluid volume. After backflow starts, the fluid near the joint intersection keeps flowing into the critically stressed fault, rather than backflows to the wellbore. Although fault slippage is affected by the changes of both pore pressure and ambient rock stress, their contributions are different at fracturing and backflow stages. At fracturing stage, pore pressure change shows a dominant effect on induced fault slippage. While at backflow stage, because the fault plane is under a critical stress state, any minor disturbance would trigger a fault slippage. The energy analysis indicates that aseismic deformation takes up a majority of the total deformation energy during hydraulic fracturing. A common regularity is found in both fracturing- and backflow-induced seismicity that the cumulative moment and maximum fault slippage are nearly proportional to the injected fluid volume. This study shows some novel insights into interpreting fracturing- and backflow-induced seismicity, and provides useful information for controlling and mitigating seismic hazards due to hydraulic fracturing.

Application of Multi-Resolution Graph-based Clustering for electrofacies prediction: a case study from the Horn River Shale, British Columbia, Canada

The Horn River Basin is a major shale gas play in British Columbia, Canada. An important component for identifying productive zones is to determine lithofacies. Quantitative core-based lithofacies classifications distinguish reservoirs and non-reservoir quality zones. In this study, Elemental capture spectroscopy (ECS) log data, XRD, and TOC data were integrated with core-description data to define the shale gas development interval in the Horn River Shale. Six lithofacies were determined and used as a training data for the supervised electrofacies classification. The Multi-Resolution Graph-based Clustering method (MRGC) was useful for prediction of electrofacies. The MRGC facilitated estimation of the electrofacies from the well logs of the non-coring wells based on our current construction model. We constructed a supervised MRGC model from well A. The probability scores of the electrofacies were 83%. This method was applied to non-coring wells B and C. Lateral variations in the facies were inferred using an electrofacies correlation or seismic-scale spatial model. Stochastic methods were applied to build a 3D facies model due to the insufficient electrofacies data. To increase the accuracy and applicability we used seismically derived-trend data (density and P-wave sonic inversion, envelope attributes) for sequential indicator simulation (SIS) modeling. After constructing a facies model, the faintly laminated siliceous mudstone (FLSM) and homogeneous siliceous mudstone (HSM) facies were shown to be dominantly distributed throughout the Evie Member and Muskwa Formation. These facies contain high silica compared to clay and higher TOC contents which are generally the main targets of a shale gas play. Therefore, the FLSM and HSM facies of the Muskwa Formation and Evie Member are potentially productive facies for shale gas development in the Horn River Shale.

How far can hydraulic fractures go? A comparative analysis of water flowback, tracer, and microseismic data from the Horn River Basin

The potential environmental impact of hydraulic fracturing is a significant public concern. A key question is: How far can fractures go beyond the well? We analyze microseismic, pressure interaction (frac-hit and interference), and tracer data recorded during fracturing of and production from 52 wells in the Horn River Basin. The results show that the wells communicate through fracture-like pathways which allow water, gas, and proppant flow. The lateral and vertical well communications occur at distances above 1 km and up to 130 m, respectively. Well communication may last for over 1.7 years possibly due to proppant flow, which prevents complete fracture closure during flowback and post-flowback processes.

Impact of natural gas production on nitrogen dioxide and sulphur dioxide over Northeast British Columbia, Canada

Northeast British Columbia (BC) Canada, is a region in which natural gas production has undergone rapid development since 2007. We used nitrogen dioxide (NO2) and sulphur dioxide (SO2) data products of the Ozone Monitoring Instrument (OMI) to assess the impact of natural gas development activity on air quality in this region from 2005 to 2018. We noticed that values of both pollutants were elevated in the immediate vicinity of large emission sources within the Montney formation and Horn River Basin – regions, which have experienced an increase in unconventional natural gas activities. Places with elevated NO2 Vertical Column Densities (VCDs) are Fort St. John, Taylor, and Dawson Creek located in the Montney formation, and higher SO2 VCDs are found near the Fort Nelson gas plant situated in the Horn River and Liard Basin areas. Although all the OMI data products consistently reported relatively high NO2 VCDs in the same areas, VCD values vary substantially with data products largely due to differences in Air Mass Factor (AMF) calculations. The rate of increase in NO2 VCDs and mass between 2005 and 2018 in the Dawson Creek area was assessed at 2.34% yr−1 and 4.32% yr−1, respectively, and these rates of change are statistically significant. Although we obtained an overall increasing trend for NO2 in Northeast BC, we also noticed a decreasing trend in the period of 2011–2013, which may be attributed, in part, to compliance and enforcement of regulations concerning flaring activities from oil and gas activities in Northeast BC, or due to less development activities. From our analysis, we suggest that the current air quality monitoring network in Northeast BC should be expanded to capture the spatial distribution of SO2 by deploying one additional station near Fort Nelson equipped with meteorology and SO2 monitoring systems.

Bayesian artificial intelligence for geologic prediction: Fracture case study, Horn River Basin

AbstractA Bayesian Belief Network (BN) has been developed to predict fractures in the subsurface during the early stages of oil and gas exploration. The probability of fractures provides a first-order proxy for spatial variations in fracture intensity at a regional scale. Nodes in the BN, representing geologic variables, were linked in a directed acyclic graph to capture key parameters influencing fracture generation over geologic time. The states of the nodes were defined by expert judgment and conditioned by available datasets. Using regional maps with public data from the Horn River Basin in British Columbia, Canada, predictions for spatial variations in the probability of fractures were generated for the Devonian Muskwa shale. The resulting BN analysis was linked to map-based predictions via a geographic information system. The automated process captures human reasoning and improves this through conditional probability calculations for a complex array of geologic influences. A comparison between inferred high fracture intensities and the locations of wells with high production rates suggests a close correspondence. While several factors could account for variations in production rates from the Muskwa shale, higher fracture densities are a likely influence. The process of constructing and cross-validating the BN supports a consistent approach to predict fracture intensities early in exploration and to prioritize data needed to improve the prediction. As such, BNs provide a mechanism to support alignment within exploration groups. As exploration proceeds, the BN can be used to rapidly update predictions. While the BN does not currently represent time-dependent processes and cannot be applied without adjustment to other regions, it offers a fast and flexible approach for fracture prediction in situations characterized by sparse data.

Integrated Interpretation of Microseismic and Petroleum-Engineering Data for Comparison of Gas Production in Two Interfering Adjacent Wellpads in the Horn River Basin, Canada

SummaryWe present an integrated interpretation of microseismic, treatment, and production data from hydraulic-fracturing jobs carried out in two adjacent wellpads in the Horn River Basin, northeast British Columbia, Canada.We conclude that poor correlation coefficients (R2) in crossplots of normalized production rate vs. the product of stimulated reservoir volume (SRV) and porosity and total organic carbon (TOC) (SRV×ϕ×TOC) indicate pressure interference between wells or wellpads. Good correlation coefficients in the same crossplots indicate lack of interference.The SRV×ϕ×TOC product reflects the hydrocarbon pore SRV because there is a relationship between TOC and hydrocarbon saturation in shales (Lopez and Aguilera 2018). Our results suggest that natural-fracture networks have an important effect on well connectivity and on the spatial distribution of microseismic data. Connectivity between wellpads occurs through a network of pre-existing natural fractures, which are approximately perpendicular to the least principal compressive stress in the area.This conclusion is supported by data analysis from Wellpads I and II in the Horn River Basin. Wellpad I includes eight wells that were drilled and fractured in the Muskwa and Otter Park formations (four wells in each formation) in 2010. Wellpad II includes three wells drilled and fractured in 2011 in each of the three shale formations, Muskwa, Otter Park, and Evie. There is a 1-year interval between fracturing on the first and second wellpads.The data analysis includes evaluation of magnitudes, b-values, moment-tensor inversion (MTI), and the spatial and temporal distributions of three-component microseismic events recorded during more than 200 stages of fracturing by multiwell downhole arrays. We analyzed Gutenberg-Richter frequency/magnitude graphs for each fracturing stage, and with proper integration of b-values, fracture-complexity index (FCI), MTI information, and treatment data, we distinguished hydraulic-fracturing-related events and events associated with slip along the surface of natural fractures. The results are compared with 5- and 4-year gas-production data in Wellpads I and II, respectively.Identification of natural fractures and information about interactions between hydraulically fractured wells are both essential for optimal well placement and completion, reservoir characterization, SRV calculation, and reservoir simulation. This study presents a distinctive insight into the integrated interpretation of microseismic events and production data to identify the activation of natural fractures and interference between the hydraulically fractured wells. The methodology developed in this study is thus related to production engineering, but examines it from the point of view of microseismic data.

History Matching and Forecast of Shale Gas Production Considering Hydraulic Fracture Closure

Most shale gas reservoirs have extremely low permeability. Predicting their fluid transport characteristics is extremely difficult due to complex flow mechanisms between hydraulic fractures and the adjacent rock matrix. Recently, studies adopting the dynamic modeling approach have been proposed to investigate the shape of the flow regime between induced and natural fractures. In this study, a production history matching was performed on a shale gas reservoir in Canada’s Horn River basin. Hypocenters and densities of the microseismic signals were used to identify the hydraulic fracture distributions and the stimulated reservoir volume. In addition, the fracture width decreased because of fluid pressure reduction during production, which was integrated with the dynamic permeability change of the hydraulic fractures. We also incorporated the geometric change of hydraulic fractures to the 3D reservoir simulation model and established a new shale gas modeling procedure. Results demonstrate that the accuracy of the predictions for shale gas flow improved. We believe that this technique will enrich the community’s understanding of fluid flows in shale gas reservoirs.